Wireless receiver apparatus

ABSTRACT

A BP detector of a wireless receiver apparatus reads a first parameter set or a second parameter set. The first parameter set includes a plurality of scaling factors and a plurality of damping factors learned together using a deep learning technique. The second parameter set includes a plurality of scaling factors and a plurality of node selection factors learned together using a deep learning technique from a memory. The BP detector executes an iterative BP algorithm that uses the first parameter set or the second parameter set in order to perform multi-user detection.

INCORPORATION BY REFERENCE

This application is based upon and claims the benefit of priority fromJapanese patent application No. 2020-072107, filed on Apr. 14, 2020, thedisclosure of which is incorporated herein in its entirety by reference.

TECHNICAL FIELD

The present disclosure relates to a radio communication system, and inparticular, to received signal processing.

BACKGROUND ART

Large-scale multi-user Multi-Input Multi-Output (MIMO) has beenimplemented in many wireless systems. The large-scale multi-user MIMO isalso referred to as massive MIMO. The large-scale multi-user MIMO (ormassive MIMO) can be used, for example, for uplink of a multiple accesscellular system such as a 5th generation (5G) system. A receiver in thelarge-scale multi-user MIMO (massive MIMO) needs to perform Multi-UserDetection (MUD) in order to separate multi-user signals.

One of the known MUD algorithms is a Belief Propagation (BP) algorithm(see, for example, Non-Patent Literature 1 and 2). The BP algorithmpropagates quality values (these are called beliefs) that represent thereliability of detection symbols between iteration processes, to therebygradually improve the detection accuracy. Practically, a detector thatuses the BP algorithm includes a Soft Interference Canceller (IC), aBelief Generator (BG)), and a Soft Replica Generator (RG). The softinterference canceller subtracts interference components from receivedsignals using replicas of respective transmission symbols obtained inthe previous iteration. The belief generator generates beliefs based onpost-cancellation signals. The soft replica generator generates replicasof transmitted signals based on the beliefs.

Techniques for improving the performance of soft decision detection ordecoding that uses the BP algorithm include damping, scaling, and nodeselection. In the damping, a weighted average of the previous beliefgenerated in the previous iteration and the current belief generated inthe current iteration is used as a new belief, thereby stabilizing thefluctuations of beliefs that cause poor convergence (see Non-PatentLiterature 1). A damping factor defines a weighting factor (orcoefficient) of the weighted average. The scaling takes into accountthat the reliability of beliefs in early iterations is relatively low,and accordingly uses a parameter (i.e., scaling factor) for adjustingthe absolute values of the beliefs so that they become gradually largeras the number of iterations increases, (see Non-Patent Literature 2). Inthe case of the MIMO detection, the node selection is used as acountermeasure against fading spatial correlations (i.e., correlationsamong receiving antennas) (see Non-Patent Literature 2). Specifically,in the node selection, a set of receiving antenna elements is dividedinto a plurality of subsets. Each subset is composed of receivingantenna elements spatially separated from each other (i.e., having lowercorrelations). The BP algorithm that involves node selection updatesonly the beliefs of one subset in each BP iteration and sequentiallyupdates the beliefs of the other subsets in the following BP iterations.

Non-Patent Literature

-   Non-Patent Literature 1: P. Som, T. Datta, A. Chockalingam and B. S.    Rajan, “Improved large-MIMO detection based on damped belief    propagation,” 2010 IEEE Information Theory Workshop on Information    Theory (ITW 2010, Cairo), Cairo, 2010, pp. 1-5.-   Non-Patent Literature 2: T. Takahashi, S. IBI and S. SAMPEI, “Design    of Criterion for Adaptively Scaled Belief in Iterative Large MIMO    Detection,” IEICE TRANSACTIONS on Communications, 2019 Volume E102.B    Issue 2 Pages 285-297

SUMMARY

The values of the damping factor and the values of the scaling factorare typically determined (or adjusted) individually and empiricallythrough, for example, computer simulations. On the other hand, theinventors have found that there is a correlation between the dampingfactor and the scaling factor and, accordingly, that an increase in therequired number of iterations or a reduction in the detectionperformance would occur unless the value of the scaling factor suitableto the value of the damping factor, or vice versa, is appropriatelyselected. However, a suitable combination of the values of theseparameters varies per iteration, and in addition there are a huge numberof candidate combinations. It is thus difficult to determine an optimalcombination of values of the scaling and damping factors used in eachiteration.

Further, as described above, the BP algorithm that involves the nodeselection uses predetermined subsets and updates the beliefs for eachsubset in a sequential manner. That is, the same division into subsetsare used through all the iterations. However, since the impact of fadingspatial correlations is mitigated as the iterations proceed and as thereliability of the beliefs improves, using a different subset periteration may contribute to reducing the total number of iterationsrequired to accomplish a desired performance. However, it is difficultto determine an optimal subset used in each iteration. In addition, whenthe scaling and the node selection are both used, it becomes moredifficult to determine both an optimal value of the scaling factor andan optimal subset (i.e., an optimal combination of receiving antennaelements whose beliefs are to be updated) used in each iteration.

One of the objects to be attained by embodiments disclosed herein is toprovide an apparatus, a method, and a program that allow a wirelessreceiver apparatus to use a near-optimal set of a scaling factor and adamping factor (or a scaling factor and a node selection factor) in eachBelief Propagation (BP) iteration. It should be noted that this objectis merely one of the objects to be attained by the embodiments disclosedherein. Other objects or problems and novel features will be madeapparent from the following description and the accompanying drawings.

In a first aspect, a wireless receiver apparatus includes at least onememory and a BP detector. The at least one memory is configured to storea first parameter set or a second parameter set. The first parameter setincludes a plurality of scaling factors and a plurality of dampingfactors learned together using a deep learning technique. The secondparameter set includes a plurality of scaling factors and a plurality ofnode selection factors learned together using a deep learning technique.The BP detector is configured to execute an iterative BP algorithm thatuses the first parameter set or the second parameter set in order toperform multi-user detection.

In a second aspect, a method performed by a wireless receiver apparatusincludes the following steps:

(a) reading a first parameter set or a second parameter set from amemory, the first parameter set including a plurality of scaling factorsand a plurality of damping factors learned together using a deeplearning technique, the second parameter set including a plurality ofscaling factors and a plurality of node selection factors learnedtogether using a deep learning technique; and

(b) executing an iterative BP algorithm that uses the first parameterset or the second parameter set in order to perform multi-userdetection.

In a third aspect, a method implemented in a computer includes thefollowing steps:

(a) receiving a set of training data, wherein each training dataincludes a plurality of transmitted signals and a plurality of receivedsignals corresponding to the plurality of transmitted signals;

(b) executing an iterative BP algorithm in the set of training data,wherein the iterative BP algorithm uses a plurality of scaling factorsand a plurality of damping factors, or uses a plurality of scalingfactors and a plurality of a node selection factors; and

(c) generating a learned set of the scaling factors and the dampingfactors, or a learned set of the scaling factors and the node selectionfactors, by training the iterative BP algorithm using a deep learningtechnique.

In a fourth aspect, a program includes instructions (software codes)that, when loaded into a computer, cause the computer to perform themethod according to the above-described second or third aspect.

BRIEF DESCRIPTION OF DRAWINGS

The above and other aspects, features, and advantages of the presentinvention will become more apparent from the following description ofcertain exemplary embodiments when taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a diagram showing a radio communication system according to anembodiment;

FIG. 2 is a diagram showing a system model according to an embodiment;

FIG. 3 is a diagram showing a configuration example of a base stationaccording to an embodiment;

FIG. 4 is a diagram showing a configuration example of a processor of abase station according to an embodiment;

FIG. 5 is a flowchart showing one example of an operation of a basestation according to an embodiment;

FIG. 6 is a diagram showing a configuration example of a BP detectoraccording to an embodiment;

FIG. 7 is a conceptual diagram showing deep unfolding of a BP detectoraccording to an embodiment;

FIG. 8 is a diagram showing one example of a lookup table according toan embodiment;

FIG. 9 is a diagram showing one example of a lookup table according toan embodiment;

FIG. 10 is a diagram showing a configuration example of a trainingsystem according to the embodiment;

FIG. 11 is a flowchart showing one example of training according to anembodiment;

FIG. 12 is a flowchart showing one example of a computer systemaccording to an embodiment; and

FIG. 13 is a diagram showing a code error rate performance of a BPdetector according to an embodiment.

EMBODIMENTS

Specific embodiments will be described hereinafter in detail withreference to the drawings. The same or corresponding elements aredenoted by the same symbols throughout the drawings, and duplicatedexplanations are omitted as necessary for the sake of clarity.

FIG. 1 shows a configuration example of a radio communication system(i.e., a multiple access cellular system) according to a plurality ofembodiments including this embodiment. Referring to FIG. 1, a basestation 1 provides wireless access for a plurality of radio terminals 2.The base station 1 may also be referred to as an access point, atransmission/reception point (TRP), or other names. The base station 1may be, for example, a gNB or a gNB Distributed Unit (gNB-DU) of a 5Gsystem. In some implementations, the radio communication system may usea multi-user MIMO technique for uplink transmissions from the pluralityof radio terminals 2 to the base station 1. In this case, the basestation 1 may receive reference signals from the radio terminals 2,estimate a MIMO channel between the radio terminals 2 and the basestation 1 using the received reference signals, receive data signalsfrom the radio terminals 2, and detect transmitted signals using theestimated channel. That is, the base station 1 may perform MIMOdetection in order to separate multi-user signals of the plurality ofradio terminals 2.

FIG. 2 shows one example of a system model of uplink multi-user MIMOtransmission. In FIG. 2, transmitters 20 of the respective radioterminals 2 communicate with a receiver 10 of the base station 1 througha channel (propagation path) 30. In the example shown in FIG. 2, each ofthe M′ transmitters 20 includes one transmitting antenna. Alternatively,each of the transmitters 20 may include two or more transmittingantennas. The receiver 10 of the base station 1 includes N′ receivingantennas. It is assumed that the total number of transmitting antennas,M′, is equal to or smaller than the total number of receiving antennas,N′.

In the following description, for the sake of simplicity, it is assumedthat the transmitted signal from each radio terminal 2 (or each user) isa single carrier transmitted signal and the propagation path betweeneach radio terminal 2 and the base station 1 is a flat-fading channel.Incidentally, in a multipath-fading environment where the transmittedsignal from each user uses Orthogonal Frequency Division Multiplexing(OFDM), Single Carrier-Frequency Division Multiple Access (SC-FDMA) orthe like, it may also be assumed that the propagation path of eachsubcarrier is a flat-fading channel by inserting a cyclic prefix havingan appropriate length into the transmitted signal. Accordingly, thisembodiment may be applied to OFDM and SC-FDMA.

Quadrature amplitude modulation (QAM) modulated transmitted signals aretransmitted from the M′ transmitting antennas of the plurality of radioterminals 2 and received by the base station 1 equipped with the N′receiving antennas. In this case, using the equivalent low-passrepresentation, a complex valued signal model can be expressed by thefollowing equation:

y ^(c) =H ^(C) x ^(c) +z ^(c)

where y^(c) is an N′×1 (i.e., N′ rows and one column) complex receivedsignal vector, H^(c) is an N′×M′ complex MIMO channel matrix, z^(c) isan N′×1 complex noise vector, and x^(c) is an M′×1 complex transmittedsignal vector.

Denoting the number of the QAM modulation symbols as Q′, Q′ is equal to4 in Quadrature Phase shift Keying (QPSK), while Q′ is equal to 16 in16QAM. It is assumed that the amplitude of the modulation symbol of eachof the I axis and the Q axis is {+c, −c} in QPSK and it is {+c, −c, +3c,−3c} in 16QAM. The value c can be expressed by the following equation,where E_(s) is average signal power. The power of complex noise in eachof the receiving antennas is denoted by N₀.

$c = \sqrt{\frac{3E_{s}}{2\left( {Q^{\prime} - 1} \right)}}$

For the sake of simplicity, a received signal model, obtained byreplacing the equivalent low-pass complex representation with anequivalent real-valued signal model y, can be expressed by the followingequations:

$\begin{matrix}{y = {{Hx} + z}} \\{y = \begin{bmatrix}{\left( y^{c} \right)} \\

\end{bmatrix}} \\{x = \begin{bmatrix}{\left( x^{c} \right)} \\

\end{bmatrix}} \\{z = \begin{bmatrix}{\left( z^{c} \right)} \\

\end{bmatrix}} \\{H = \left\lbrack {\begin{matrix}{\left( H^{c} \right)} \\

\end{matrix}\begin{matrix}{- \left( H^{c} \right)} \\{\left( H^{c} \right)}\end{matrix}} \right\rbrack}\end{matrix}$

where y is an N×1 equivalent real-valued received signal vector, H is anN×M equivalent real-valued MIMO channel matrix, z is an N×1 equivalentreal-valued noise vector, and x is an M×1 equivalent real-valuedtransmitted signal vector. The value N is equal to 2N′, while the valueM is equal to 2M′. Each transmitted signal is equivalent to a PulseAmplitude Modulation (PAM) modulated symbol having the number of themodulation symbols Q equal to √Q′ (i.e., square root of Q′), while theaverage signal power is E_(s)/2. Further, noise power included in eachelement of the noise vector z is N₀/2. The following provides adescription of reception processing using the equivalent real-valuedmodel.

FIG. 3 shows a configuration example of the base station 1. Referring toFIG. 3, the base station 1 includes a Radio Frequency (RF) transceiver301, a network interface 303, a processor 304, and a memory 305. The RFtransceiver 301 performs analog RF signal processing in order tocommunicate with the radio terminals 2. The RF transceiver 301 mayinclude a plurality of transceivers. The RF transceiver 301 is coupledto an antenna array 302 and the processor 304. The RF transceiver 301receives modulated symbol data from the processor 304, generates atransmission RF signal, and supplies the transmission RF signal to theantenna array 302. Further, the RF transceiver 301 generates a basebandreceived signal based on a received RF signal received by the antennaarray 302 and supplies the baseband received signal to the processor304. The RF transceiver 301 may include an analog beamformer circuit forbeam forming. The analog beamformer circuit includes, for example, aplurality of phase shifters and a plurality of power amplifiers.

The network interface 303 is used to communicate with network nodes(e.g., another base station and a core network node). The networkinterface 303 may include, for example, a network interface card (NIC)conforming to the IEEE 802.3 series.

The processor 304 performs digital baseband signal processing (i.e.,data-plane processing) and control-plane processing for radiocommunication. The processor 304 may include a plurality of processors.The processor 304 may include, for example, a modem processor (e.g., aCentral Processing Unit (CPU), a graphics processing unit (GPU), or aDigital Signal Processor (DSP)) that performs digital baseband signalprocessing and a protocol stack processor (e.g., a Central ProcessingUnit (CPU) or a Micro Processing Unit (MPU)) that performs thecontrol-plane processing.

The digital baseband signal processing by the processor 304 may include,for example, signal processing of a Service Data Adaptation Protocol(SDAP) layer, a Packet Data Convergence Protocol (PDCP) layer, a RadioLink Control (RLC) layer, a Medium Access Control (MAC) layer, and aPhysical (PHY) layer. Further, the control-plane processing performed bythe processor 304 may include processing of Non-Access Stratum (NAS)messages, Radio Resource Control (RRC) messages, Medium Access Control(MAC) Control Elements (CEs), and Downlink Control Information (DCI).

The processor 304 may include a digital beamformer module for beamforming. The digital beamformer module may include a MIMO encoder and apre-coder.

The memory 305 is composed of a combination of a volatile memory and anon-volatile memory. The volatile memory is, for example, a StaticRandom Access Memory (SRAM), a Dynamic RAM (DRAM), or a combinationthereof. The non-volatile memory is, for example, a mask Read OnlyMemory (MROM), an Electrically Erasable Programmable ROM (EEPROM), aflash memory, a hard disc drive, or any combination thereof. The memory305 may include a storage located apart from the processor 304. In thiscase, the processor 304 may access the memory 305 via the networkinterface 303 or another I/O interface.

The memory 305 may include a computer readable medium storing one ormore software modules (computer programs) including instructions anddata to perform at least a part of processing by the base station 1. Insome implementations, the processor 304 may be configured to load thesoftware modules from the memory 305 and execute the loaded softwaremodules, thereby performing at least a part of the processing by thebase station 1.

According to this embodiment, the processor 304 causes the base station1 to perform received signal processing for multi-user detection (MIMOdetection). For this purpose, the processor 304 may include a BPdetector 400 and a decision and demodulation module 460 shown in FIG. 4.

The BP detector 400 receives N equivalent real-valued received signals,y₁ to y_(N), obtained by the N′ receiving antennas and executes theiterative BP algorithm with the total number of iterations T in order toperform multi-user detection. After that, the BP detector 400 providesthe decision and demodulation module 460 with the estimated values, r₁^((T)) to r_(M) ^((T)), regarding M separated equivalent real-valuedtransmitted signals.

In some implementations, the BP detector 400 uses a first parameter setin the BP algorithm. The first parameter set includes a plurality ofscaling factors and a plurality of damping factors learned together (orconcurrently) using a deep learning technique. The BP detector 400 usesthe plurality of scaling factors in different respective iterations ofthe BP algorithm. Likewise, the BP detector 400 uses the plurality ofdamping factors in the different respective iterations of the BPalgorithm. Accordingly, the total number of scaling factors and thetotal number of damping factors may each be equal to the total number ofiterations of the BP algorithm.

In other implementations, the BP detector 400 uses a second parameterset in the BP algorithm. The second parameter set includes a pluralityof scaling factors and a plurality of node selection factors learnedtogether using a deep learning technique. The BP detector 400 uses theplurality of scaling factors in the different iterations of the BPalgorithm. Likewise, the BP detector 400 uses the plurality of nodeselection factors in different iterations of the BP algorithm. As willbe described later, the BP algorithm may use a plurality of nodeselection factors per iteration. In this case, the second parameter setmay include a set of node selection factors per iteration.

The first parameter set (or the second parameter set) is stored in thememory 305 of the base station 1. As shown in FIG. 4, the firstparameter set (or the second parameter set) may be stored in the memory305 as a lookup table (LUT) 450.

FIG. 5 shows one example of the operation of the base station 1. In Step501, the processor 304 (e.g., BP detector 400) of the base station 1reads from the memory 305 the scaling factors and the damping factors(or the scaling factors and the node selection factors), which have beenlearned together. In Step 502, the processor 304 (e.g., the BP detector400) receives the received signals y₁-y_(N) received by the antenna 302via the RF transceiver 301. In Step 503, the processor 304 (e.g., the BPdetector 400) performs the BP algorithm while updating the scalingfactor and the damping factor (or the scaling factor and the nodeselection factor) per iteration and generates the estimated values r₁^((T)) to r_(M) ^((T)) of the M separated transmitted signals. Afterthat, the processor 304 (e.g., the decision and demodulation module 460)decodes transmitted signals of all the M users based on the estimatedvalues r₁ ^((T)) to r_(M) ^((T)).

As will be understood from the above description, in this embodiment,the BP detector 400 of the base station 1 uses the scaling factors andthe damping factors learned together using a deep learning technique.Alternatively, the BP detector 400 uses the scaling factors and the nodeselection factors learned together using a deep learning technique.According to this embodiment, it thus becomes possible to allow the basestation 1 to use a near-optimal set of a scaling factor and a dampingfactor (or a scaling factor and a node selection factor) in each BPiteration.

The following provides a description of a configuration example of theBP detector 400. FIG. 6 shows a configuration example of the BP detector400. Referring to FIG. 6, the BP detector 400 includes N softinterference cancellers 610-1 to 610-N, a belief generator 620, and Nsoft replica generators 630-1 to 630-N. The soft interference cancellers610-1 to 610-N respectively receive the N received signals y₁-y_(N)obtained by the N receiving antennas. The soft interference canceller610-1 receives, for example, the received signal y₁ of a first antenna(this signal is referred to as a first received signal). In addition, inorder to perform the t-th iteration, the soft interference canceller610-1 receives soft replicas x hat_(1,1) ^((t-1)) to x hat_(1,M)^((t-1)) of all the transmitted signals generated in the previous(t−1)-th iteration. Here, x hat means x with circumflex ({circumflexover ( )}). The soft interference canceller 610-1 then generatespost-cancellation received signals y tilde_(1,1) ^((t)) to y tilde_(1,M)^((t)). Here, y tilde means y with a tilde (˜) above.

The belief generator 620 reads the damping factors (or the sets of nodeselection factors) included in the above-described first parameter set(or the second parameter set) from the LUT 450. The belief generator 620receives the post-cancellation received signals y tilde_(1,1) ^((t)) toy tilde_(1,M) ^((t)) from the soft interference canceller 610-1. Thebelief generator 620 also receives post-cancellation received signals ytilde_(n,1) ^((t)) to y tilde_(n,M) ^((t)) similarly generated by eachof the remaining soft interference cancellers 610-n (where n is between2 and N). Then the belief generator 620 generates beliefs r_(1,1) ^((t))to r_(1,M) ^((t)) associated with the first received signal using thedamping factor (or the set of node selection factors) for the t-thiteration. Likewise, the belief generator 620 generates beliefsassociated with each of the remaining second to n-th received signals.

The soft replica generator 630-1 reads the scaling factors included inthe above-described first parameter set (or the second parameter set)from the LUT 450. The soft replica generator 630-1 receives the beliefsr_(1,1) ^((t)) to r_(1,M) ^((t)) associated with the first receivedsignal from the belief generator 620. Then the soft replica generator630-1 generates soft replicas x hat_(1,1) ^((t)) to x hat_(1,M) ^((t))and further generates soft replica's power p_(1,1) ^((t)) to p_(1,M)^((t)), using the scaling factor for the t-th iteration.

After the completion of the BP processing with the total number ofiterations T, the belief generator 620 determines the estimated valuesr₁ ^((T)) to r_(M) ^((T)) of the M separated transmitted signals andprovides these estimated values for the decision and demodulation module460.

The following provides further details of the processing performed bythe soft interference canceller 610, the belief generator 620, and thesoft replica generator 630.

(1) Soft Interference Canceller

In the first iteration, soft replicas have not yet been generated. Thesoft interference canceller 610 thus supplies the first to N-th receivedsignals to the belief generator 620 without performing cancellationprocessing. In the t-th iteration, which is the second or any subsequentiteration, the soft interference canceller 610-n associated with then-th received signal cancels M−1 transmitted signal components otherthan the m-th transmitted signal from the n-th received signal andgenerates the post-cancellation received signal y tilde_(n,m) ^((t)).The post-cancellation received signal y tilde_(n,m) ^((t)) is given bythe following equation:

${\overset{\sim}{y}}_{n,m}^{(t)} = {y_{n} - {\sum\limits_{{j = 1}{j \neq m}}^{M}{h_{n,j}{\hat{x}}_{n,j}^{({t - 1})}}}}$

where y_(n) is the received signal of the n-th receiving antenna,h_(n,j) is a channel response between the j-th transmitting antenna andthe n-th receiving antenna, and x hat_(n,j) ^((t-1)) is a soft replicaof the transmitted signal of the j-th transmitting antenna obtained inthe (t−1)-th iteration processing. As described above, the base station1 is able to estimate the channel response using the reference signaltransmitted from the radio terminal 2. The post-cancellation receivedsignal y tilde_(n,m) ^((t)) is supplied to the belief generator 620.

(2) Belief Generator

The belief generator 620 generates beliefs using the post-cancellationreceived signals. First, the belief generator 620 performs processingexpressed by the following equation using the post-cancellation receivedsignal y tilde_(n,m) ^((t)) regarding the n-th receiving antenna,thereby obtaining a transmitted signal component s_(n,m) ^((t)) in thet-th iteration:

$s_{n,m}^{(t)} = \frac{h_{n,m}{\hat{y}}_{n,m}^{(t)}}{\psi_{n,m}^{(t)}}$

where ψ_(n,m) ^((t)) is a residual interference and noise power. Theresidual interference and noise power ψ_(n,m) ^((t)) is obtained by thefollowing equations:

$\psi_{n,m}^{(t)} = {{\sum\limits_{{j = 1}{j \neq m}}^{M}{h_{n,j}^{2}\delta_{n,j}^{(t)}}} + \frac{N_{0}}{2}}$δ_(n, j)^((t)) = p_(n, j)^((t − 1)) − (x̂_(n, j)^((t − 1)))²

where p_(n,j) ^((t-1)) is the power of the soft replica. As describedabove, the soft replica's power is generated by the soft replicagenerator 630.

The equivalent gain ω_(n,m) ^((t)) to the true transmitted signal x_(m)included in the transmitted signal component s_(n,m) ^((t)) is used fornormalization in the scaling processing and is given by the followingequation:

$\omega_{n,m}^{(t)} = {\frac{h_{n,m}^{2}}{\psi_{n,m}^{(t)}}.}$

Next, the belief generator 620 generates a belief r_(n,m) ^((t)) usingthe transmitted signal component s_(n,m) ^((t)). The belief generator620 uses either the damping processing or the node selection processing.The damping processing calculates the weighted average of thetransmitted signal component obtained in the previous (t−1)-th iterationand the transmitted signal component obtained in the current t-thiteration by using the damping factor η^((t)) as follows:

${s^{\prime}}_{n,m}^{(t)} = {{\eta^{(t)}{\sum\limits_{{i = 1}{i \neq n}}^{N}s_{i,m}^{(t)}}} + {\left( {1 - \eta^{(t)}} \right){s^{\prime}}_{n,m}^{({t - 1})}}}$

where s′_(n,m) ^((t)) is a transmitted signal component after thedamping processing. As a result of this damping processing, theequivalent gain included in s′_(n,m) ^((t)) is given by the followingequation:

${\omega^{\prime}}_{n,m}^{(t)} = {{\eta^{(t)}{\sum\limits_{{i = 1}{i \neq n}}^{N}\omega_{i,m}^{(t)}}} + {\left( {1 - \eta^{(t)}} \right){\omega^{\prime}}_{n,m}^{({t - 1})}}}$

On the other hand, in the node selection, s′_(n,m) ^((t)) is calculatedby synthesizing the transmitted signal components of the antennasobtained in the latest K iterations, which is given by the followingequation:

${{s^{\prime}}_{n,m}^{(t)} = {\sum\limits_{k = 0}^{K - 1}{\sum\limits_{{i = 1}{i \neq n}}^{N}{\eta_{i,{t - k}}^{(t)}s_{i,m}^{({t - k})}}}}},{{s.t.{\sum\limits_{k = 0}^{K - 1}\eta_{i,{t - k}}^{(t)}}} = 1}$

where η_(i,t-k) ^((t)) is a node selection factor indicating how muchthe transmitted signal component s_(i,m) ^((t-k)) is considered in thet-th iteration. In the existing node selection method, the value of thenode selection factor η_(i,t-k) ^((t)) is either 0 or 1, which meansthat it is alternatively determined whether or not to take the node i(i.e., observation node, receiving antenna) into account. On the otherhand, in this embodiment, the node selection factor η_(i,t-k) ^((t)) isa real number value between 0 and 1 (or a real number value not lessthan 0 and not greater than 1). Accordingly, the node selection factorη_(i,t-k) ^((t)) of this embodiment is able to finely adjust how muchthe transmitted signal component s_(i,m) ^((t-k)) of the node i isconsidered in the t-th iteration. Besides, the node selection factorη_(i,t-k) ^((t)) of this embodiment is learnable (or trainable) in deeplearning, as will be described later. When K=t in the above expression,the transmitted signal components obtained in all the past iterationsare used in the node selection.

As a result of the node selection processing, the equivalent gainincluded in s′_(n,m) ^((t)) is given by the following equation:

${\omega^{\prime}}_{n,m}^{(t)} = {\sum\limits_{k = 0}^{K - 1}{\sum\limits_{{i = 1}{i \neq n}}^{N}{\eta_{i,{t - k}}^{(t)}\omega_{i,m}^{(t)}}}}$

The belief generator 620 normalizes s′_(n,m) ^((t)) obtained by eitherthe damping or the node selection with ω′_(n,m) ^((t)), therebygenerating a normalized belief r_(n,m) ^((t)). The belief generator 620supplies the normalized belief to the soft replica generator 630. Thenormalized belief r_(n,m) ^((t)) is expressed by the following equation:

$r_{n,m}^{(t)} = {\frac{{s^{\prime}}_{n,m}^{(t)}}{{\omega^{\prime}}_{n,m}^{(t)}}.}$

(3) Soft Replica Generator

The soft replica generator 630 scales the belief r_(n,m) ^((t)) with ascaling factor a^((t)) and calculates the soft replica x hat_(n,m)^((t)) and soft replica's power p_(n,m) ^((t)) in accordance with thefollowing equations:

${\overset{\hat{}}{x}}_{n,m}^{(t)} = {c{\sum\limits_{s^{\prime} \in S_{Q^{\prime}}}{\tan\;{h\left( {\frac{a^{(t)}}{c}\left( {r_{n,m}^{(t)} - s^{\prime}} \right)} \right)}}}}$$p_{n,m}^{(t)} = {E_{s}^{\max} + {2c{\sum\limits_{s^{\prime} \in S_{Q^{\prime}}}{s^{\prime}\tan\;{h\left( {\frac{a^{(t)}}{c}\left( {r_{n,m}^{(t)} - s^{\prime}} \right)} \right)}}}}}$

where E_(s) ^(max) is the energy of the largest PAM symbol possible ands′ is a determination threshold for PAM modulation. The value E_(s)^(max) is given by the following equation:

E _(s) ^(max)=(√{square root over (Q′)}−1)² c ²

The determination threshold s′ can have any value of a set S_(Q′). Theset S_(Q′) is {0} for QPSK and {0, +2c, −2c} for 16QAM. The tan hfunction is a hyperbolic tangent function. These equations indicate thatthe soft replica x hat_(n,m) ^((t)) and the soft replica's power p_(n,m)^((t)) are generated by synthesizing belief information around thedetermination threshold.

(4) Output of BP Detector

After the completion of the T iterations, the belief generator 620supplies the estimated value r_(m) ^((T)) of each of the M separatedtransmitted signals to the decision and demodulation module 460. Theestimated value r_(m) ^((T)) is given by the following equation:

${r_{m}^{(T)} = {\left( {\sum\limits_{i = 1}^{N}s_{i,m}^{(T)}} \right)/\left( {\sum\limits_{i = 1}^{N}\omega_{i,m}^{(T)}} \right)}}.$

The following provides a description of a method of learning theparameters to be used by the processor 304 (the BP detector 400) toperform the BP algorithm. FIG. 7 is a conceptual diagram showing deepunfolding for the multi-user detection based on the BP. The deepunfolding is a method of unfolding the iterative algorithm in theiterative direction, assuming that the obtained process flow graph is aDeep Neural Network (DNN), and using a scheme of deep learning. The BPnetwork shown in FIG. 7 is given by unfolding the iterations performedby the BP detector 400. Each BP iteration corresponds to one layer ofthe DNN. Accordingly, it is possible to learn meta parameters embeddedin the BP network. Specifically, the learnable (or trainable) parametersare the scaling factor a^((t)) and the damping factor q^((t)) in eachiteration (or each layer), or the scaling factor a^((t)) and the set{η_(i,t-k) ^((t))} of node selection factors in each iteration (or eachlayer). The learning is performed, for example, based on a gradientmethod and the meta parameters are adjusted together in the direction inwhich the cost becomes smaller.

FIG. 8 shows one example of sets of the scaling factor and the dampingfactor obtained by deep learning. These learned parameters may be storedin the memory 305 as an LUT 450. The table shown in FIG. 8 storeslearned parameters for the total numbers of BP iterations T of threevalues (i.e., 3, 8, and 16). In this case, the processor 304 (the BPdetector 400) of the base station 1 may use in the iterative BPalgorithm the subset of parameters corresponding to the configured (orselected) number of iterations T. The parameters for the number ofiterations T include a scaling factor a_(T) ^((t)) and a damping factorη_(T) ^((t)) per iteration. The subscripts for these parameters indicatethe total number of iterations. Specifically, when the total number ofiterations T is 3, the learned parameters include: a set of a scalingfactor a₃ ⁽¹⁾ and a damping factor η₃ ⁽¹⁾ for the first iteration; a setof a scaling factor a₃ ⁽²⁾ and a damping factor η₃ ⁽²⁾ for the seconditeration; and a set of a scaling factor a₃ ⁽³⁰⁾ and a damping factor η₃⁽³⁾ for the third iteration.

FIG. 9 shows one example of combinations of a scaling factor and nodeselection factors obtained by deep learning. Like in the table shown inFIG. 8, the table shown in FIG. 9 stores learned parameters for thecases that the total numbers of BP iterations T are 3, 8, and 16. Theparameters for the number of iterations T include a scaling factor a_(T)^((t)) and a set {η_(T,t-k) ^((t))} of node selection factors periteration.

The examples shown in FIGS. 8 and 9 are merely examples. In anotherexample, the BP detector 400 may operate only with one fixed value ofthe total number of iterations. In this case, the base station 1 may besupplied only with the learned parameters for this value of the totalnumber of iterations. Alternatively, the base station 1 may be suppliedwith learned parameters for four or more values of the total number ofiterations.

FIG. 10 shows one example of the training system environment. A trainingdata set 1010 includes a transmitted signal data set 1012 and a receivedsignal data set 1014. The transmitted signal data set 1012 may berandomly generated. The received signal data set 1014 corresponds to thetransmitted signal data set 1012 and is generated using the transmittedsignal data set 1012 and a given channel matrix. The channel matrix maybe randomly generated or may be generated based on a propagation pathmodel defined in the 3rd Generation Partnership Project (3GPP)specifications or the like. Alternatively, the channel matrix may begenerated based on measurement results in the actual environment wherethe base station 1 is installed.

A training system 1020 includes a BP detector module 1022 and a learningmodule 1024. The BP detector module 1022 emulates the processor 304 orthe BP detector 400 of the base station 1. The BP detector module 1022is able to execute a BP algorithm that is the same as the BP algorithmimplemented in the base station 1. The learning module 1024 trains theBP detector module 1022 using the training data set 1010. The learningmodule 1024 may apply one or more machine learning algorithms.

In one example, the learning module 1024 may use an update algorithm,such as a gradient method. The gradient update method to be used may be,for example, an Adaptive moment estimation (Adam) optimizer algorithm.In addition, the learning module 1024 may use mini-batch learning. Thenumber of learning iterations may be set to an appropriate value in viewthe risk of overfitting to the training data. To update the learningrate, a Step algorithm that gradually narrows the update width withrespect to the number of learning iterations may be used. The costfunction may be a Mean Square Error (MSE).

The learning module 1024 outputs a trained parameter set 1030 obtainedby machine learning. The trained parameter set 1030 includes a pluralityof scaling factors and a plurality of damping factors (or a plurality ofscaling factors and a plurality of node selection factors).

FIG. 11 shows one example of an operation of the training system 1020.In Step 1101, the training system 1020 receives the training data set1010. In Step 1102, the training system 1020 performs, on the trainingdata set 1010, an iterative BP algorithm that uses scaling factors anddamping factors and trains the iterative BP algorithm using a deeplearning technique. The trainable parameters are the scaling factors andthe damping factors. In Step 1103, the training system 1020 stores thetrained scaling factors and damping factors in a memory.

Alternatively, in Step 1102, the training system 1020 may perform aniterative BP algorithm that uses scaling factors and node selectionfactors, on the training data set 1010. In this case, the trainableparameters are the scaling factors and the node selection factors and,in Step 1103, the training system 1020 stores the trained scalingfactors and node selection factors in a memory.

The training system 1020 may be a computer system as shown in FIG. 12.FIG. 12 shows a configuration example of a computer system 1200. Thecomputer system 1200 is able to execute one or more computer programsincluding a set of instructions, thereby performing, for example, amethod for the training system 1020. The training system 1020 may be astandalone computer or may include one or more networked computers. Thecomputer system 1200 may be one or both of a server and a client in aserver-client environment. The computer system 1200 may be a personalcomputer, a tablet computer, or a smartphone.

In the example shown in FIG. 12, the computer system 1200 includes oneor more processors 1210, a memory 1220, and a mass storage 1230, whichcommunicate with one another via a bus 1270. The one or more processors1210 may include, for example, one or both of a central processingunit(s) (CPU(s)) and a graphics processing unit(s) (GPU(s)). Thecomputer system 1200 may include other devices, such as one or moreoutput devices 1240, one or more input devices 1250, and one or moreperipherals 1260. The one or more output devices 1240 include, forexample, a video display and a speaker. The one or more input devices1250 include, for example, a keyboard, a mouse, a keypad, a touch pad, atouch screen, or any combination thereof. One or more peripherals 1260include, for example, a printer, a modem, a network adapter, or anycombination thereof.

One or both of the memory 1220 and the mass storage 1230 include acomputer readable medium storing one or more sets of instructions. Theseinstructions may be partially or fully stored in a memory in theprocessor 1210. These instructions, when executed on the processor 1210,cause the processor 120 to perform, for example, the machine learningprocess described with reference to FIG. 11.

As described above, in some implementations, the processor 304 includedin the base station 1 executes one or more programs includinginstructions for causing a computer to execute the algorithm describedin this embodiment. In addition, the training system 1020 executes oneor more programs including instructions for causing a computer toexecute machine learning described in this embodiment. Each of theseprograms can be stored and provided to a computer using any type ofnon-transitory computer readable media. Non-transitory computer readablemedia include any type of tangible storage media. Examples ofnon-transitory computer readable media include magnetic storage media(such as flexible disks, magnetic tapes, hard disk drives, etc.),optical magnetic storage media (e.g., magneto-optical disks), CompactDisc Read Only Memory (CD-ROM), CD-R, CD-R/W, and semiconductor memories(such as mask ROM, Programmable ROM (PROM), Erasable PROM (EPROM), flashROM, Random Access Memory (RAM), etc.). Each of the programs may beprovided to a computer using any type of transitory computer readablemedia. Examples of transitory computer readable media include electricsignals, optical signals, and electromagnetic waves. Transitory computerreadable media can provide the program to a computer via a wiredcommunication line (e.g., electric wires, and optical fibers) or awireless communication line.

FIG. 13 shows comparison in bit error rate (BER) performance between theBP detector 400 according to this embodiment and an existing linearminimum mean square error (MMSE) detector. These are simulation resultsof the multi-user MIMO configuration of (N′, M′)=(32, 28), where thenumber of terminals is denoted by M′ and the number of receiving antennaelements is denoted by N′. Note that the total number of iterations (T)is set to 16. The graph 1310 shows the BER of the BP detector that usesa parameter set of the learned damping factors and scaling factorsdescribed in this embodiment. On the other hand, the graph 1320 is acomparative example and shows the BER of a BP detector that does not usethe parameter set of the learned damping factors and scaling factors.The graph 1330 is another comparative example and shows the BER of theLinear MMSE. These results confirmed that the BP detector of thisembodiment can significantly reduce the error floor level, and also canimprove the BER performance more greatly than the MMSE detector.

An example advantage according to the above-described embodiments is toallow the wireless receiver apparatus to use a near-optimal set of thescaling factor and the damping factor (or the scaling factor and thenode selection factor) in each BP iteration.

The above-described embodiment is merely an example of applications ofthe technical ideas obtained by the inventors. These technical ideas arenot limited to the above-described embodiment and various modificationscan be made thereto.

The whole or part of the exemplary embodiments disclosed above can bedescribed as, but not limited to, the following supplementary notes.

(Supplementary Note 1)

A wireless receiver apparatus comprising:

at least one memory configured to store a first parameter set or asecond parameter set, the first parameter set including a plurality ofscaling factors and a plurality of damping factors learned togetherusing a deep learning technique, the second parameter set including aplurality of scaling factors and a plurality of node selection factorslearned together using a deep learning technique; and

a BP detector configured to execute an iterative Belief Propagation (BP)algorithm that uses the first parameter set or the second parameter setin order to perform multi-user detection.

(Supplementary Note 2)

The wireless receiver apparatus according to Supplementary Note 1,wherein

the BP detector is configured to use the scaling factors in differentrespective iterations of the iterative BP algorithm, and

the BP detector is configured to use the damping factors, or the nodeselection factors, in the different respective iterations of theiterative BP algorithm.

(Supplementary Note 3)

The wireless receiver apparatus according to Supplementary Note 1 or 2,wherein

the first parameter set, or the second parameter set, comprises aplurality of subsets that correspond to different total numbers ofiterations, and

the BP detector is configured to use in the iterative BP algorithm asubset that corresponds to a configured number of iterations.

(Supplementary Note 4)

The wireless receiver apparatus according to any one of SupplementaryNotes 1 to 3, wherein

the BP detector is configured to use the second parameter set in theiterative BP algorithm, and

each of the node selection factors is a real number value between 0 and1.

(Supplementary Note 5)

The wireless receiver apparatus according to any one of SupplementaryNotes 1 to 4, wherein the BP detector comprises:

an interference canceller configured to use replicas of all transmittedsignals, excluding an m-th transmitted signal, generated in a (t−1)-thiteration and subtract components of all the transmitted signals,excluding a component of the m-th transmitted signal, from an n-threceived signal among a plurality of received signals, therebygenerating a post-cancellation n-th received signal;

a belief generator configured to generate a belief associated with then-th received signal at least based on the damping factor or the nodeselection factor and based on the post-cancellation n-th receivedsignal; and

a replica generator configured to generate a replica of the m-thtransmitted signal in a t-th iteration at least based on the scalingfactor and the belief.

(Supplementary Note 6)

A method performed in a wireless receiver apparatus, the methodcomprising:

reading a first parameter set or a second parameter set from a memory,the first parameter set including a plurality of scaling factors and aplurality of damping factors learned together using a deep learningtechnique, the second parameter set including a plurality of scalingfactors and a plurality of node selection factors learned together usinga deep learning technique; and

executing an iterative Belief Propagation (BP) algorithm that uses thefirst parameter set or the second parameter set in order to performmulti-user detection.

(Supplementary Note 7)

The method according to Supplementary Note 6, wherein the executingcomprises:

using the scaling factors in different respective iterations of theiterative BP algorithm; and using the damping factors, or the nodeselection factors, in the different respective iterations of theiterative BP algorithm.

(Supplementary Note 8)

The method according to Supplementary Note 6 or 7, wherein

the first parameter set, or the second parameter set, comprises aplurality of subsets that correspond to different total numbers ofiterations, and

the executing comprises using in the iterative BP algorithm a subsetthat corresponds to a configured number of iterations.

(Supplementary Note 9)

The method according to any one of Supplementary Notes 6-8, wherein

the executing comprises using the second parameter set in the iterativeBP algorithm, and

each of the node selection factors is a real number value between 0 and1.

(Supplementary Note 10)

A program for causing a processor included in a wireless receiverapparatus to:

read a first parameter set or a second parameter set from a memory, thefirst parameter set including a plurality of scaling factors and aplurality of damping factors learned together using a deep learningtechnique, the second parameter set including a plurality of scalingfactors and a plurality of node selection factors learned together usinga deep learning technique; and

execute an iterative Belief Propagation (BP) algorithm that uses thefirst parameter set or the second parameter set in order to performmulti-user detection.

(Supplementary Note 11)

A method implemented in a computer, the method comprising:

receiving a set of training data, wherein each training data comprises aplurality of transmitted signals and a plurality of received signalscorresponding to the plurality of transmitted signals;

executing an iterative BP algorithm in the set of training data, whereinthe iterative BP algorithm uses a plurality of scaling factors and aplurality of damping factors, or uses a plurality of scaling factors anda plurality of a node selection factors; and

generating a learned set of the scaling factors and the damping factors,or a learned set of the scaling factors and the node selection factors,by training the iterative BP algorithm using a deep learning technique.

What is claimed is:
 1. A wireless receiver apparatus comprising: atleast one memory configured to store a first parameter set or a secondparameter set, the first parameter set including a plurality of scalingfactors and a plurality of damping factors learned together using a deeplearning technique, the second parameter set including a plurality ofscaling factors and a plurality of node selection factors learnedtogether using a deep learning technique; and a BP detector configuredto execute an iterative Belief Propagation (BP) algorithm that uses thefirst parameter set or the second parameter set in order to performmulti-user detection.
 2. The wireless receiver apparatus according toclaim 1, wherein the BP detector is configured to use the scalingfactors in different respective iterations of the iterative BPalgorithm, and the BP detector is configured to use the damping factors,or the node selection factors, in the different respective iterations ofthe iterative BP algorithm.
 3. The wireless receiver apparatus accordingto claim 1, wherein the first parameter set, or the second parameterset, comprises a plurality of subsets that correspond to different totalnumbers of iterations, and the BP detector is configured to use in theiterative BP algorithm a subset that corresponds to a configured numberof iterations.
 4. The wireless receiver apparatus according to claim 1,wherein the BP detector is configured to use the second parameter set inthe iterative BP algorithm, and each of the node selection factors is areal number value between 0 and
 1. 5. The wireless receiver apparatusaccording to claim 1, wherein the BP detector comprises: an interferencecanceller configured to use replicas of all transmitted signals,excluding an m-th transmitted signal, generated in a (t−1)-th iterationand subtract components of all the transmitted signals, excluding acomponent of the m-th transmitted signal, from an n-th received signalamong a plurality of received signals, thereby generating apost-cancellation n-th received signal; a belief generator configured togenerate a belief associated with the n-th received signal at leastbased on the damping factor or the node selection factor and based onthe post-cancellation n-th received signal; and a replica generatorconfigured to generate a replica of the m-th transmitted signal in at-th iteration at least based on the scaling factor and the belief.
 6. Amethod performed by a wireless receiver apparatus, the methodcomprising: reading a first parameter set or a second parameter set froma memory, the first parameter set including a plurality of scalingfactors and a plurality of damping factors learned together using a deeplearning technique, the second parameter set including a plurality ofscaling factors and a plurality of node selection factors learnedtogether using a deep learning technique; and executing an iterativeBelief Propagation (BP) algorithm that uses the first parameter set orthe second parameter set in order to perform multi-user detection. 7.The method according to claim 6, wherein the executing comprises: usingthe scaling factors in different respective iterations of the iterativeBP algorithm; and using the damping factors, or the node selectionfactors, in the different respective iterations of the iterative BPalgorithm.
 8. The method according to claim 6, wherein the firstparameter set, or the second parameter set, comprises a plurality ofsubsets that correspond to different total numbers of iterations, andthe executing comprises using in the iterative BP algorithm a subsetthat corresponds to a configured number of iterations.
 9. The methodaccording to claim 6, wherein the executing comprises using the secondparameter set in the iterative BP algorithm, and each of the nodeselection factors is a real number value between 0 and
 1. 10. Anon-transitory computer readable medium storing a program comprisinginstructions, which when executed on a processor of a wireless receiverapparatus causes the processor to: read a first parameter set or asecond parameter set from a memory, the first parameter set including aplurality of scaling factors and a plurality of damping factors learnedtogether using a deep learning technique, the second parameter setincluding a plurality of scaling factors and a plurality of nodeselection factors learned together using a deep learning technique; andexecute an iterative Belief Propagation (BP) algorithm that uses thefirst parameter set or the second parameter set in order to performmulti-user detection.
 11. The non-transitory computer readable mediumaccording to claim 10, wherein the executing comprises: using thescaling factors in different respective iterations of the iterative BPalgorithm; and using the damping factors, or the node selection factors,in the different respective iterations of the iterative BP algorithm.12. The non-transitory computer readable medium according to claim 10,wherein the first parameter set, or the second parameter set, comprisesa plurality of subsets that correspond to different total numbers ofiterations, and the executing comprises using in the iterative BPalgorithm a subset that corresponds to a configured number ofiterations.
 13. The non-transitory computer readable medium according toclaim 10, wherein the executing comprises using the second parameter setin the iterative BP algorithm, and each of the node selection factors isa real number value between 0 and 1.